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MapReduce >> mail # user >> will an application with two maps but no reduce be suitable for hadoop?


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Re: will an application with two maps but no reduce be suitable for hadoop?
On Thu, Apr 18, 2013 at 4:49 AM, Hadoop Explorer
<[EMAIL PROTECTED]> wrote:
> I have an application that evaluate a graph using this algorithm:
>
> - use a parallel for loop to evaluate all nodes in a graph (to evaluate a
> node, an image is read, and then result of this node is calculated)
>
> - use a second parallel for loop to evaluate all edges in the graph.  The
> function would take in results from both nodes of the edge, and then
> calculate the answer for the edge
>
>
> As you can see, the above algorithm would employ two map functions, but no
> reduce function.  The total data size can be very large (say 100GB).  Also,
> the workload of each node and each edge is highly irregular, and thus load
> balancing mechanisms are essential.
>
> In this case, will hadoop suit this application?  if so, how will the
> architecture of my program like?  And will hadoop be able to strike the
> balance between a good load balancing of the second map function, and
> minimizing data transfer of the results from the first map function?

map-only jobs are known in Hadoop ecosystem. For example, that's how
Giraph implements BSP on top of Hadoop. In fact, from what you're
describing it sounds like Giraph could be a good fit. Check it out:
    http://giraph.apache.org/

Thanks,
Roman.
NEW: Monitor These Apps!
elasticsearch, apache solr, apache hbase, hadoop, redis, casssandra, amazon cloudwatch, mysql, memcached, apache kafka, apache zookeeper, apache storm, ubuntu, centOS, red hat, debian, puppet labs, java, senseiDB